
import os


import geopandas as gpd
from  fiona import listlayers

#maps
brazilMaps='https://github.com/CienciaDeDatosEspacial/geodfprepro/raw/main/maps/brazilMaps_5641.gpkg'

#layers in maps
listlayers(brazilMaps)

['country', 'cities', 'rivers']


brazil=gpd.read_file(brazilMaps,layer='country')
brazil_cities=gpd.read_file(brazilMaps,layer='cities')
brazil_rivers=gpd.read_file(brazilMaps,layer='rivers')


brazil.crs.to_epsg()==brazil_cities.crs.to_epsg()==brazil_rivers.crs.to_epsg()

True


# plotting tinee que tner el mismo cr para que cada layer salga encima de otro ene la psoicion correcta
base = brazil.plot(facecolor="greenyellow")
brazil_rivers.plot(edgecolor='blue', linewidth=0.5,ax=base)
brazil_cities.plot(marker='+', color='red', markersize=15,ax=base)

<Axes: >


brazil


brazil.boundary #por eso es multistring porque estoy pidiendo que gurade el borde del poligono
#me da un valor es como un dao el excel, es como que me da un valor dame la media y me da 3.5
#son diefentes estrcuturas de datas y por elllo aplicaremos cierto tipo de funciones dependiedno de la estrcutura

0    MULTILINESTRING ((1926257.542 8894978.397, 192...
dtype: geometry


# This is just the borderline
brazil.boundary.plot()

<Axes: >


# does 'boundary' return a GDF?
type(brazil.boundary)

geopandas.geoseries.GeoSeries


# converting into GDF
brazil.boundary.to_frame() #aqui lo he convertido en una tabla geodataframe
#esto es un gdf a partir de una geoseries
#le puedo decri a pyhton datos cual es la columna que tiene datos geometricos indepedneitemente de su nombre 'geometrías'
#por si acaso la geometria es 'geometrías'
#me asegruo de decirle cual es la geometria


# conversion
brazil_border=brazil.boundary.to_frame() 

# new column (optional)
brazil_border['name']='Brazil' 

# renaming the geometry column
brazil_border.rename(columns={0:'geometry'},inplace=True) 

#setting the geometry (the name is not enough)
brazil_border = brazil_border.set_geometry("geometry")

# verifying:
brazil_border.crs

<Projected CRS: EPSG:5641>
Name: SIRGAS 2000 / Brazil Mercator
Axis Info [cartesian]:
- X[east]: Easting (metre)
- Y[north]: Northing (metre)
Area of Use:
- name: Brazil - offshore - equatorial margin.
- bounds: (-51.64, -5.74, -32.43, 7.04)
Coordinate Operation:
- name: Petrobras Mercator
- method: Mercator (variant B)
Datum: Sistema de Referencia Geocentrico para las AmericaS 2000
- Ellipsoid: GRS 1980
- Prime Meridian: Greenwich


brazil_border #en la tarea tine que parecere tal cual asi geometry name y multistring


brazil_states=gpd.read_file(os.path.join("maps","bra_adm_ibge_2020_shp","bra_admbnda_adm1_ibge_2020.shp"))
brazil_municipalities=gpd.read_file(os.path.join("maps","bra_adm_ibge_2020_shp","bra_admbnda_adm2_ibge_2020.shp"))


brazil_states.crs, brazil_municipalities.crs

(None, None)


import matplotlib.pyplot as plt

fig, (ax1, ax2) = plt.subplots(ncols=2, sharex=False, sharey=False, figsize=(12,12))

brazil_states.plot(ax=ax1, facecolor='pink', edgecolor='brown')
brazil_municipalities.plot(ax=ax2, facecolor='pink', edgecolor='black',linewidth=0.2)

<Axes: >


 brazil_states.to_crs(5641)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[23], line 1
----> 1 brazil_states.to_crs(5641)

File C:\ProgramData\anaconda3\envs\SDS_Mi_ambiente_Romina\Lib\site-packages\geopandas\geodataframe.py:1425, in GeoDataFrame.to_crs(self, crs, epsg, inplace)
   1423 else:
   1424     df = self.copy()
-> 1425 geom = df.geometry.to_crs(crs=crs, epsg=epsg)
   1426 df.geometry = geom
   1427 if not inplace:

File C:\ProgramData\anaconda3\envs\SDS_Mi_ambiente_Romina\Lib\site-packages\geopandas\geoseries.py:1157, in GeoSeries.to_crs(self, crs, epsg)
   1078 def to_crs(
   1079     self, crs: Optional[Any] = None, epsg: Optional[int] = None
   1080 ) -> GeoSeries:
   1081     """Returns a ``GeoSeries`` with all geometries transformed to a new
   1082     coordinate reference system.
   1083 
   (...)
   1154 
   1155     """
   1156     return GeoSeries(
-> 1157         self.values.to_crs(crs=crs, epsg=epsg), index=self.index, name=self.name
   1158     )

File C:\ProgramData\anaconda3\envs\SDS_Mi_ambiente_Romina\Lib\site-packages\geopandas\array.py:855, in GeometryArray.to_crs(self, crs, epsg)
    786 """Returns a ``GeometryArray`` with all geometries transformed to a new
    787 coordinate reference system.
    788 
   (...)
    852 
    853 """
    854 if self.crs is None:
--> 855     raise ValueError(
    856         "Cannot transform naive geometries.  "
    857         "Please set a crs on the object first."
    858     )
    859 if crs is not None:
    860     crs = CRS.from_user_input(crs)

ValueError: Cannot transform naive geometries.  Please set a crs on the object first.


brazil_states.geometry.head()

0    MULTIPOLYGON (((-68.87747 -11.01987, -68.88027...
1    POLYGON ((-35.46317 -8.82467, -35.46457 -8.828...
2    MULTIPOLYGON (((-50.46147 2.11133, -50.45627 2...
3    MULTIPOLYGON (((-58.49367 -0.84197, -58.48917 ...
4    MULTIPOLYGON (((-38.70687 -17.96447, -38.70867...
Name: geometry, dtype: geometry


brazil_states.crs = "EPSG:4326"
brazil_municipalities.crs = "EPSG:4326"


brazil_states=brazil_states.to_crs(5641)
brazil_municipalities=brazil_municipalities.to_crs(5641)


import pandas as pd 
infoairports=pd.read_csv(os.path.join("data","br-airports.csv"))

# see

infoairports
#EL QUE TENGA CORRDENADAS NO LO HAVE UN OBEJTO ESPACIAL
#Es importante porque en algun momneto mr van a dar coordenadas y tengo que saber como convetir


# bye first row 
infoairports.drop(index=0,inplace=True)
infoairports.reset_index(drop=True, inplace=True)

# keep the  columns needed
keep=['name','type','latitude_deg', 'longitude_deg','elevation_ft','region_name','municipality']
infoairports=infoairports.loc[:,keep]
infoairports.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6866 entries, 0 to 6865
Data columns (total 7 columns):
 #   Column         Non-Null Count  Dtype 
---  ------         --------------  ----- 
 0   name           6866 non-null   object
 1   type           6866 non-null   object
 2   latitude_deg   6866 non-null   object
 3   longitude_deg  6866 non-null   object
 4   elevation_ft   6696 non-null   object
 5   region_name    6866 non-null   object
 6   municipality   6842 non-null   object
dtypes: object(7)
memory usage: 375.6+ KB


numericCols=['latitude_deg', 'longitude_deg','elevation_ft']
infoairports[numericCols]=infoairports.loc[:,numericCols].apply(lambda x:pd.to_numeric(x))

# now 
infoairports.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6866 entries, 0 to 6865
Data columns (total 7 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   name           6866 non-null   object 
 1   type           6866 non-null   object 
 2   latitude_deg   6866 non-null   float64
 3   longitude_deg  6866 non-null   float64
 4   elevation_ft   6696 non-null   float64
 5   region_name    6866 non-null   object 
 6   municipality   6842 non-null   object 
dtypes: float64(3), object(4)
memory usage: 375.6+ KB


# let's plot

base = brazil.plot(color='green', edgecolor='black') #unprojected

infoairports.plot.scatter(x = 'longitude_deg', y = 'latitude_deg',ax=base)

<Axes: xlabel='longitude_deg', ylabel='latitude_deg'>


airports=gpd.GeoDataFrame(data=infoairports.copy(),
                 geometry=gpd.points_from_xy(infoairports.longitude_deg,
                                             infoairports.latitude_deg), 
                 crs=4326)# the coordinates were in degrees - unprojected
#simepre q alg diga longitud latitud es q est a no protyecatdi
#e 5341 es la proyeccion del mapa de brasil


airports=airports.to_crs(5641)


# let's plot

base = brazil.plot(color='white', edgecolor='green')
airports.plot(ax=base,markersize=1)

<Axes: >


#remember:
type(airports), type(infoairports)

(geopandas.geodataframe.GeoDataFrame, pandas.core.frame.DataFrame)


airports['type'].value_counts() # this will not work: airports.type.value_counts()
#recomendacon es no ponerle type como nmbre de columna porque de por si type es una funcion q te indica el tipo de dato

type
small_airport     4713
heliport          1745
closed             272
medium_airport     124
large_airport       10
seaplane_base        2
Name: count, dtype: int64


airports.rename(columns={'type':'kind'},inplace=True)


from folium import LayerControl


m = airports[airports.kind=='small_airport'].explore(color="red",name="small",show=False)
m = airports[airports.kind=='medium_airport'].explore(m=m, color="blue",name="medium",show=False)
m = airports[airports.kind=='large_airport'].explore(m=m, color="black",name="large",show=True)
m = airports[airports.kind=='seaplane_base'].explore(m=m, color="green",name="seaplane",show=False)
m = airports[airports.kind=='closed'].explore(m=m, color="white",name="closed",show=False)
m = airports[airports.kind=='heliport'].explore(m=m, color="orange",name="heliport",show=False)

LayerControl(collapsed=False).add_to(m) #optional

m
#lo q se nos ocurra q sean puntos, buscar un csv que diga COORDINATES, ej. colegios, hospitales, aeropuertos


centroBrazil=brazil.centroid# es una geoserie es un valor calculado
centroBrazil

0    POINT (3884486.179 8756856.093)
dtype: geometry


type(centroBrazil), type(centroBrazil.x)

(geopandas.geoseries.GeoSeries, pandas.core.series.Series)


type(centroBrazil.x[0]),type(centroBrazil.y[0])
#dependiendo de lo que me pide la funcion, si admite geoseries ponerle geoseries, avces puede funcionar con otro tipo de dato
#pero me puede mandar un warning y mejor ponerle su tipo de dato tla cual pide

(numpy.float64, numpy.float64)


fig, axs = plt.subplots(nrows=2,ncols=2,sharex=False, sharey=False,figsize=(8,8))

centroidX=centroBrazil.x[0]
centroidY=centroBrazil.y[0]

axs[0,0].set_title('LeftOf_Centroid, Above_Centroid',fontsize=5)
brazil_municipalities.cx[:centroidX,centroidY:].plot(ax=axs[0,0]) 

axs[0,1].set_title('RightOf_Centroid, Above_Centroid',fontsize=5)
brazil_municipalities.cx[centroidX:,centroidY:].plot(ax=axs[0,1]) 

axs[1,0].set_title('LeftOf_Centroid, Below_Centroid',fontsize=5)
brazil_municipalities.cx[:centroidX,:centroidY].plot(ax=axs[1,0]) 

axs[1,1].set_title('RightOf_Centroid, Below_Centroid',fontsize=5)
brazil_municipalities.cx[centroidX:,:centroidY].plot(ax=axs[1,1]) ;


#profesor dijo que se puede poner con ciudades o con minucipapldfaed


#instead of:
brazil_municipalities.cx[:centroidX,centroidY:]#el cx es de geopandas solo lo entiende geopandas , pandas no
#el cx necesita un valor


# dissolving
brazil_municipalities.cx[:centroidX,centroidY:].dissolve()


type(brazil_municipalities.cx[:centroidX,centroidY:].dissolve())
#algunas funcoines devuelven geoseries, esta devuelve un dataframe

geopandas.geodataframe.GeoDataFrame


brazil_municipalities.cx[:centroidX,centroidY:].dissolve().plot(edgecolor='red')

<Axes: >


# the municipalities by state
brazil_municipalities.cx[:centroidX,centroidY:].dissolve(by='ADM1_PT').plot(edgecolor='red')
#si no le idgo nada disucleve todo y si le digo disuelve por grupos junsta todas las geometrias 
#los ha juntado por estados

<Axes: >


brazil_municipalities.cx[:centroidX,centroidY:].unary_union


type(brazil_municipalities.cx[:centroidX,centroidY:].unary_union)
#normalenete uno hace algo para combinarlo con otras cosas
#entnces para eso necesitamos usar estrufruas conocidas
#o sea puede ser que este mapa que me arrojo se vea bien, pero mejor converetilo a una estructura concocida
#el unary union es una geometria y calcula rapido te devuleve shaping

shapely.geometry.multipolygon.MultiPolygon


gpd.GeoDataFrame(index=[0],
                 crs=brazil_municipalities.crs,
                 geometry=[brazil_municipalities.cx[:centroidX,centroidY:].unary_union])
#lo pongo como una lista por si hay mas geometrias


# you create
Brazil_topLeft=brazil_municipalities.cx[:centroidX,centroidY:].dissolve()
# you get
Brazil_topLeft
#el disolve te devulve dataframe pero te agrag columnas q no sirven a vces


#por qué?


Brazil_topLeft['zone']='NW' # add column
Brazil_topLeft=Brazil_topLeft.loc[:,['ADM0_EN','zone','geometry']]   # keep some

# result
Brazil_topLeft


Brazil_topRight=brazil_municipalities.cx[centroidX:,centroidY:].dissolve()
Brazil_bottomLeft=brazil_municipalities.cx[:centroidX,:centroidY].dissolve()
Brazil_bottomRight=brazil_municipalities.cx[centroidX:,:centroidY].dissolve()

Brazil_topRight['zone']='NE'
Brazil_topRight=Brazil_topRight.loc[:,['ADM0_EN','zone','geometry']] 

Brazil_bottomLeft['zone']='NE'
Brazil_bottomLeft=Brazil_bottomLeft.loc[:,['ADM0_EN','zone','geometry']] 

Brazil_bottomRight['zone']='NE'
Brazil_bottomRight=Brazil_bottomRight.loc[:,['ADM0_EN','zone','geometry']]


brazil_municipalities


# tuple with count of rows
rowsCX=(brazil_municipalities.cx[:centroBrazil.x[0],centroBrazil.y[0]:].shape[0],\
        brazil_municipalities.cx[centroBrazil.x[0]:,centroBrazil.y[0]:].shape[0],\
        brazil_municipalities.cx[:centroBrazil.x[0],:centroBrazil.y[0]].shape[0],\
        brazil_municipalities.cx[centroBrazil.x[0]:,:centroBrazil.y[0]].shape[0])

sum(rowsCX)
#como hay cosas(municipalidades) q se repiten entonces al sumar salen menos 5753

5753


# coincidences:
len(set(brazil_municipalities.cx[:centroBrazil.x[0],centroBrazil.y[0]:].ADM2_PCODE). \
intersection(set(brazil_municipalities.cx[centroBrazil.x[0]:,centroBrazil.y[0]:].ADM2_PCODE)). \
intersection(set(brazil_municipalities.cx[:centroBrazil.x[0],:centroBrazil.y[0]].ADM2_PCODE)). \
intersection(set(brazil_municipalities.cx[centroBrazil.x[0]:,:centroBrazil.y[0]].ADM2_PCODE)))

1


brazil_municipalities.shape[0]

5572


base=Brazil_topLeft.plot(facecolor='grey', alpha=0.4)
Brazil_topRight.plot(ax=base,facecolor='orange', alpha=0.4)
Brazil_bottomLeft.plot(ax=base,facecolor='green', alpha=0.4)
Brazil_bottomRight.plot(ax=base,facecolor='red', alpha=0.4)

<Axes: >


Brazil_AirTopLeft=airports[airports.kind=='medium_airport'].cx[:centroidX,centroidY:]
Brazil_AirTopRight=airports[airports.kind=='medium_airport'].cx[centroidX:,centroidY:]
Brazil_AirBottomLeft=airports[airports.kind=='medium_airport'].cx[:centroidX,:centroidY]
Brazil_AirBottomRight=airports[airports.kind=='medium_airport'].cx[centroidX:,:centroidY]


base=Brazil_AirTopLeft.plot(facecolor='grey', alpha=0.4)
Brazil_AirTopRight.plot(ax=base,facecolor='orange', alpha=0.4)
Brazil_AirBottomLeft.plot(ax=base,facecolor='green', alpha=0.4)
Brazil_AirBottomRight.plot(ax=base,facecolor='red', alpha=0.4)

<Axes: >


Brazil_AirBottomLeft


Brazil_AirBottomLeft.convex_hull.plot()

<Axes: >


Brazil_AirBottomLeft.dissolve().convex_hull.plot()

<Axes: >


Brazil_AirBottomLeft.dissolve().convex_hull

0    POLYGON ((3763743.842 6340901.399, 3438220.327...
dtype: geometry


# SW of Brazil as convex hull
Brazil_bottomLeft.convex_hull.plot()

<Axes: >


brazil_municipalities.cx[:centroidX,:centroidY].convex_hull.plot(edgecolor='red')

<Axes: >


# just the union
large_airport=airports[airports.kind=='large_airport']
large_airport.unary_union


# hull of the union
large_airport.unary_union.convex_hull


LargeAirport_hull= gpd.GeoDataFrame(index=[0],
                                    crs=large_airport.crs,
                                    geometry=[large_airport.unary_union.convex_hull])
LargeAirport_hull['name']='large airports hull' # optional

# then

LargeAirport_hull


base=brazil.plot(facecolor='yellow')
large_airport.plot(ax=base)
LargeAirport_hull.plot(ax=base,facecolor='green',
                       edgecolor='white',alpha=0.4,
                       hatch='X')
#como aptrapo en una geomtria ese es el convex hull, basicamente para un conjunto de coordendas encontrar lo que le envulve
#no puede dejar a nadie a fuera inclye a los que no son convex hull
#ls importancia es que nos ayuda a visulizar en donde estan la maypr cantidad e aeropuertos

<Axes: >


# the north
MunisN_brazil=brazil_municipalities.cx[:,centroidY:]
# the south
MunisS_brazil=brazil_municipalities.cx[:,:centroidY]
# the west
MunisW_brazil=brazil_municipalities.cx[:centroidX,:]
# the east
MunisE_brazil=brazil_municipalities.cx[centroidX:,:]


munisMidNS_brazil=MunisN_brazil.overlay(MunisS_brazil, how="intersection",keep_geom_type=True)
munisMidNS_brazil

C:\ProgramData\anaconda3\envs\SDS_Mi_ambiente_Romina\Lib\site-packages\geopandas\geodataframe.py:1815: FutureWarning: `unary_union` returned None due to all-None GeoSeries. In future, `unary_union` will return 'GEOMETRYCOLLECTION EMPTY' instead.
  merged_geom = block.unary_union


MunisN_brazil.sjoin(MunisS_brazil, how="inner", predicate='contains')


# keeping the overlay
munisMidWE_brazil=MunisW_brazil.overlay(MunisE_brazil, how="intersection",keep_geom_type=True)
munisMidWE_brazil.plot(edgecolor='white',linewidth=0.1)

C:\ProgramData\anaconda3\envs\SDS_Mi_ambiente_Romina\Lib\site-packages\geopandas\geodataframe.py:1815: FutureWarning: `unary_union` returned None due to all-None GeoSeries. In future, `unary_union` will return 'GEOMETRYCOLLECTION EMPTY' instead.
  merged_geom = block.unary_union

<Axes: >


munisMidNS_brazil.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 65 entries, 0 to 64
Data columns (total 17 columns):
 #   Column        Non-Null Count  Dtype   
---  ------        --------------  -----   
 0   ADM0_EN_1     65 non-null     object  
 1   ADM0_PT_1     65 non-null     object  
 2   ADM0_PCODE_1  65 non-null     object  
 3   ADM1_PT_1     65 non-null     object  
 4   ADM1_PCODE_1  65 non-null     object  
 5   ADM2_PT_1     65 non-null     object  
 6   ADM2_PCODE_1  65 non-null     object  
 7   ET_ID_1       65 non-null     int64   
 8   ADM0_EN_2     65 non-null     object  
 9   ADM0_PT_2     65 non-null     object  
 10  ADM0_PCODE_2  65 non-null     object  
 11  ADM1_PT_2     65 non-null     object  
 12  ADM1_PCODE_2  65 non-null     object  
 13  ADM2_PT_2     65 non-null     object  
 14  ADM2_PCODE_2  65 non-null     object  
 15  ET_ID_2       65 non-null     int64   
 16  geometry      65 non-null     geometry
dtypes: geometry(1), int64(2), object(14)
memory usage: 8.8+ KB


munisMidWE_brazil.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 115 entries, 0 to 114
Data columns (total 17 columns):
 #   Column        Non-Null Count  Dtype   
---  ------        --------------  -----   
 0   ADM0_EN_1     115 non-null    object  
 1   ADM0_PT_1     115 non-null    object  
 2   ADM0_PCODE_1  115 non-null    object  
 3   ADM1_PT_1     115 non-null    object  
 4   ADM1_PCODE_1  115 non-null    object  
 5   ADM2_PT_1     115 non-null    object  
 6   ADM2_PCODE_1  115 non-null    object  
 7   ET_ID_1       115 non-null    int64   
 8   ADM0_EN_2     115 non-null    object  
 9   ADM0_PT_2     115 non-null    object  
 10  ADM0_PCODE_2  115 non-null    object  
 11  ADM1_PT_2     115 non-null    object  
 12  ADM1_PCODE_2  115 non-null    object  
 13  ADM2_PT_2     115 non-null    object  
 14  ADM2_PCODE_2  115 non-null    object  
 15  ET_ID_2       115 non-null    int64   
 16  geometry      115 non-null    geometry
dtypes: geometry(1), int64(2), object(14)
memory usage: 15.4+ KB


keep=['ADM0_EN_1','ADM1_PT_1','ADM2_PT_1','geometry']
munisMidNS_brazil=munisMidNS_brazil.loc[:,keep]
munisMidWE_brazil=munisMidWE_brazil.loc[:,keep]


# now 
munisMidNS_brazil.overlay(munisMidWE_brazil,how="union",keep_geom_type=True)
#creo otracolumna ha duplicado pero el union solo trabaja con geometrias


# appending
pd.concat([munisMidNS_brazil,munisMidWE_brazil],ignore_index=True)


munisMidNS_brazil.dissolve().overlay(munisMidWE_brazil.dissolve(), how="union",keep_geom_type=True).dissolve().plot()

<Axes: >


muniMidBrazil=munisMidNS_brazil.dissolve().overlay(munisMidWE_brazil.dissolve(), how="union",keep_geom_type=True).dissolve()
muniMidBrazil


# some cleaning

muniMidBrazil['zone']='middles'
muniMidBrazil=muniMidBrazil.loc[:,['ADM0_EN_1_1','zone','geometry']]   
muniMidBrazil


# with the municipalities
brazil_municipalities.overlay(muniMidBrazil, how='difference').plot()

<Axes: >


# with the zones
Brazil_topLeft.overlay(muniMidBrazil, how='difference').plot()

<Axes: >


MunisN_brazil.overlay(MunisS_brazil, how="symmetric_difference",keep_geom_type=False).plot()

<Axes: >


MunisW_brazil.overlay(MunisE_brazil, how="symmetric_difference",keep_geom_type=False).plot()

<Axes: >


# hulls for the mid size airports:
Brazil_AirTopLeft_hull=Brazil_AirTopLeft.dissolve().convex_hull
Brazil_AirTopRight_hull=Brazil_AirTopRight.dissolve().convex_hull
Brazil_AirBottomLeft_hull=Brazil_AirBottomLeft.dissolve().convex_hull
Brazil_AirBottomRight_hull=Brazil_AirBottomRight.dissolve().convex_hull


base = brazil.plot(color='white', edgecolor='black') #unprojected
muniMidBrazil.plot(ax=base,facecolor='magenta',alpha=0.4) #unprojected
LargeAirport_hull.plot(ax=base,facecolor='purple',alpha=0.4)
Brazil_AirTopLeft_hull.plot(ax=base,facecolor='grey', alpha=0.4)
Brazil_AirTopRight_hull.plot(ax=base,facecolor='orange', alpha=0.4)
Brazil_AirBottomLeft_hull.plot(ax=base,facecolor='green', alpha=0.4)
Brazil_AirBottomRight_hull.plot(ax=base,facecolor='red', alpha=0.4)

<Axes: >


# non valid
brazil_municipalities[~brazil_municipalities.is_valid]


# see the invalid:
brazil_municipalities[~brazil_municipalities.is_valid].plot()

<Axes: >


# what is wrong?

from shapely.validation import explain_validity, make_valid

explain_validity(brazil_municipalities[~brazil_municipalities.is_valid].geometry)

5       Ring Self-intersection[3068508.44978257 851673...
15      Ring Self-intersection[3065079.35271118 868017...
16      Ring Self-intersection[2697459.77578448 903418...
22      Ring Self-intersection[2708460.04757785 872708...
36      Ring Self-intersection[2793277.57533772 901160...
                              ...                        
5464    Ring Self-intersection[4134149.59432842 826732...
5470    Ring Self-intersection[4579443.31264052 849053...
5471    Ring Self-intersection[4016405.94439198 821413...
5496    Ring Self-intersection[4404321.70180657 831135...
5512    Ring Self-intersection[4261277.42674537 804649...
Name: geometry, Length: 256, dtype: object


# varieties?
brazil_municipalities['validity']=[x.split('[')[0] for x in brazil_municipalities.geometry.apply(lambda x: explain_validity(x))]
brazil_municipalities['validity'].value_counts()

validity
Valid Geometry            5316
Ring Self-intersection     256
Name: count, dtype: int64


# solving the issue:
brazil_municipalities.drop(columns=['validity'],inplace=True)

brazil_municipalities_valid=brazil_municipalities.copy()

brazil_municipalities_valid['geometry'] = [make_valid(row)  if not row.is_valid else row for row in brazil_municipalities_valid['geometry'] ]
#any invalid?
brazil_municipalities_valid[~brazil_municipalities_valid.is_valid]


[x for x in brazil_municipalities_valid["geometry"]]

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 <POLYGON ((5283189.096 9689969.622, 5282992.177 9689604.958, 5282713.775 968...>,
 <POLYGON ((5423490.093 9207817.323, 5423435.771 9207497.797, 5423225.272 920...>,
 <POLYGON ((5393789.359 9299521.884, 5393986.278 9299196.122, 5393857.262 929...>,
 <POLYGON ((5458684.173 9270867.879, 5458847.14 9270521.578, 5459261.347 9270...>,
 <POLYGON ((5549619.753 9437921.27, 5550088.283 9437521.734, 5551106.827 9437...>,
 <POLYGON ((5404103.812 9477186.942, 5403635.282 9476733.475, 5403743.926 947...>,
 <POLYGON ((5495915.339 9573156.389, 5495338.165 9573345.679, 5494903.586 957...>,
 <POLYGON ((5485050.873 9561108.617, 5484636.666 9560364.876, 5484134.184 956...>,
 <POLYGON ((5324419.744 9655666.206, 5325580.884 9654720.51, 5329627.897 9649...>,
 <POLYGON ((5269866.544 9653126.319, 5269988.77 9652633.196, 5270043.092 9652...>,
 <MULTIPOLYGON (((5295649.28 9628359.394, 5296287.568 9624008.047, 5296477.69...>,
 <POLYGON ((5487386.734 9199876.166, 5487685.506 9199407, 5487841.683 9198842...>,
 ...]


pd.Series([type(x) for x in brazil_municipalities_valid.geometry]).value_counts()

<class 'shapely.geometry.polygon.Polygon'>                  5104
<class 'shapely.geometry.multipolygon.MultiPolygon'>         456
<class 'shapely.geometry.collection.GeometryCollection'>      12
Name: count, dtype: int64


# brazil.to_file(os.path.join("maps","brazilMaps_5641_all.gpkg"),layer='country', driver="GPKG")
# brazil_cities.to_file(os.path.join("maps","brazilMaps_5641_all.gpkg"),layer='cities', driver="GPKG")
# brazil_rivers.to_file(os.path.join("maps","brazilMaps_5641_all.gpkg"),layer='rivers', driver="GPKG")
# airports.to_file(os.path.join("maps","brazilMaps_5641_all.gpkg"),layer='airports', driver="GPKG")
# brazil_border.to_file(os.path.join("maps","brazilMaps_5641_all.gpkg"), layer='border', driver="GPKG")
# brazil_states.to_file(os.path.join("maps","brazilMaps_5641_all.gpkg"), layer='states', driver="GPKG")
# brazil_municipalities.to_file(os.path.join("maps","brazilMaps_5641_all.gpkg"), layer='municipalities', driver="GPKG")

	id	ident	type	name	latitude_deg	longitude_deg	elevation_ft	continent	country_name	iso_country	...	municipality	scheduled_service	gps_code	iata_code	local_code	home_link	wikipedia_link	keywords	score	last_updated
0	#meta +id	#meta +code	#loc +airport +type	#loc +airport +name	#geo +lat	#geo +lon	#geo +elevation +ft	#region +continent +code	#country +name	#country +code +iso2	...	#loc +municipality +name	#status +scheduled	#loc +airport +code +gps	#loc +airport +code +iata	#loc +airport +code +local	#meta +url +airport	#meta +url +wikipedia	#meta +keywords	#meta +score	#date +updated
1	5910	SBGR	large_airport	Guarulhos - Governador André Franco Montoro In...	-23.431944	-46.467778	2461	SA	Brazil	BR	...	São Paulo	1	SBGR	GRU	SP0002	http://www.aeroportoguarulhos.net/	https://en.wikipedia.org/wiki/S%C3%A3o_Paulo-G...	Cumbica	1016675	2021-10-28T15:52:55+00:00
2	5906	SBGL	large_airport	Rio Galeão – Tom Jobim International Airport	-22.809999	-43.250557	28	SA	Brazil	BR	...	Rio De Janeiro	1	SBGL	GIG	RJ0001	NaN	https://en.wikipedia.org/wiki/Rio_de_Janeiro-G...	Galeão - Antônio Carlos Jobim International Ai...	51475	2021-10-14T18:09:35+00:00
3	5974	SBSP	medium_airport	Congonhas Airport	-23.627657	-46.654601	2631	SA	Brazil	BR	...	São Paulo	1	SBSP	CGH	SP0001	http://www.infraero.gov.br/usa/aero_prev_home....	https://en.wikipedia.org/wiki/Congonhas-S%C3%A...	http://www.infraero.gov.br/usa/aero_prev_home....	750	2022-05-03T20:10:35+00:00
4	5967	SBRJ	medium_airport	Santos Dumont Airport	-22.9105	-43.163101	11	SA	Brazil	BR	...	Rio de Janeiro	1	SBRJ	SDU	RJ0002	https://www4.infraero.gov.br/aeroportos/aeropo...	https://en.wikipedia.org/wiki/Santos_Dumont_Ai...	RIO	750	2022-03-28T23:27:00+00:00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6862	309669	SSVR	closed	Volta Redonda Airport	-22.4978	-44.085	1245	SA	Brazil	BR	...	Volta Redonda	0	NaN	NaN	NaN	NaN	NaN	SSVR, SSVR, QVR	0	2013-09-28T14:52:12+00:00
6863	505308	BR-1760	heliport	Unimed Volta Redonda Helipad	-22.533988	-44.076918	1385	SA	Brazil	BR	...	Volta Redonda	0	SSKV	NaN	RJ0147	NaN	NaN	NaN	0	2022-12-06T21:28:25+00:00
6864	341727	BR-1429	heliport	Santa Helena Heliport	-23.59851	-47.441196	2254	SA	Brazil	BR	...	Votorantim	0	SWHE	NaN	SP0807	NaN	NaN	NaN	0	2021-03-07T10:30:07+00:00
6865	343017	BR-1493	heliport	Bandeiras Centro Empresarial Heliport	-23.536615	-47.449475	1827	SA	Brazil	BR	...	Votorantim	0	SWST	NaN	SP1306	NaN	NaN	NaN	0	2021-04-14T20:12:01+00:00
6866	509863	SN3D	heliport	Alphaville Nova Esplanada 2 Heliport	-23.558971	-47.473779	2083	SA	Brazil	BR	...	Votorantim	0	SN3D	NaN	SP1453	NaN	NaN	NaN	0	2023-06-30T14:01:13+00:00

	ADM0_EN	ADM0_PT	ADM0_PCODE	ADM1_PT	ADM1_PCODE	ADM2_PT	ADM2_PCODE	ET_ID	geometry
1	Brazil	Brasil	BR	Rondônia	BR11	Ariquemes	BR1100023	1	POLYGON ((2839173.154 8911097.984, 2838718.204...
3	Brazil	Brasil	BR	Rondônia	BR11	Cacoal	BR1100049	3	POLYGON ((2997393.730 8777661.276, 2997393.730...
8	Brazil	Brasil	BR	Rondônia	BR11	Espigão D'Oeste	BR1100098	8	POLYGON ((3057637.194 8777929.305, 3056978.535...
9	Brazil	Brasil	BR	Rondônia	BR11	Guajará-Mirim	BR1100106	9	MULTIPOLYGON (((2708460.048 8727087.796, 27089...
10	Brazil	Brasil	BR	Rondônia	BR11	Jaru	BR1100114	10	POLYGON ((2871236.909 8863979.571, 2871101.103...
...	...	...	...	...	...	...	...	...	...
5305	Brazil	Brasil	BR	Mato Grosso	BR51	São Félix do Araguaia	BR5107859	5305	POLYGON ((3974265.397 8773180.057, 3974197.494...
5310	Brazil	Brasil	BR	Mato Grosso	BR51	Tabaporã	BR5107941	5310	POLYGON ((3505714.931 8812411.339, 3505972.962...
5313	Brazil	Brasil	BR	Mato Grosso	BR51	Terra Nova do Norte	BR5108055	5313	POLYGON ((3647944.371 8863821.903, 3657430.408...
5321	Brazil	Brasil	BR	Mato Grosso	BR51	Nova Guarita	BR5108808	5321	POLYGON ((3638016.966 8872883.250, 3638098.449...
5324	Brazil	Brasil	BR	Mato Grosso	BR51	Nova Monte Verde	BR5108956	5324	POLYGON ((3449722.190 8896410.060, 3449477.739...

	ADM0_EN	ADM0_PT	ADM0_PCODE	ADM1_PT	ADM1_PCODE	ADM2_PT	ADM2_PCODE	ET_ID	geometry
0	Brazil	Brasil	BR	Rondônia	BR11	Alta Floresta D'Oeste	BR1100015	0	POLYGON ((2880702.575 8678970.180, 2881137.154...
1	Brazil	Brasil	BR	Rondônia	BR11	Ariquemes	BR1100023	1	POLYGON ((2839173.154 8911097.984, 2838718.204...
2	Brazil	Brasil	BR	Rondônia	BR11	Cabixi	BR1100031	2	POLYGON ((3067184.343 8504324.153, 3067191.133...
3	Brazil	Brasil	BR	Rondônia	BR11	Cacoal	BR1100049	3	POLYGON ((2997393.730 8777661.276, 2997393.730...
4	Brazil	Brasil	BR	Rondônia	BR11	Cerejeiras	BR1100056	4	POLYGON ((2974496.868 8540812.592, 2974897.495...
...	...	...	...	...	...	...	...	...	...
5567	Brazil	Brasil	BR	Goiás	BR52	Vianópolis	BR5222005	5567	POLYGON ((4414833.073 8123005.730, 4414826.282...
5568	Brazil	Brasil	BR	Goiás	BR52	Vicentinópolis	BR5222054	5568	MULTIPOLYGON (((4227475.357 8023245.907, 42270...
5569	Brazil	Brasil	BR	Goiás	BR52	Vila Boa	BR5222203	5569	POLYGON ((4542830.062 8359934.818, 4543400.447...
5570	Brazil	Brasil	BR	Goiás	BR52	Vila Propício	BR5222302	5570	POLYGON ((4375754.947 8322581.505, 4376087.671...
5571	Brazil	Brasil	BR	Distrito Federal	BR53	Brasília	BR5300108	5571	POLYGON ((4513061.426 8221825.541, 4512959.571...

	name	kind	latitude_deg	longitude_deg	elevation_ft	region_name	municipality	geometry
8	Cataratas International Airport	medium_airport	-25.594167	-54.489444	786.0	Paraná	Foz do Iguaçu	POINT (3721774.865 7071323.040)
15	Campo Grande Airport	medium_airport	-20.469998	-54.673988	1833.0	Mato Grosso do Sul	Campo Grande	POINT (3701243.952 7687543.733)
22	Marechal Rondon Airport	medium_airport	-15.652900	-56.116699	617.0	Mato Grosso	Cuiabá	POINT (3540739.278 8248021.581)
52	Coronel Adalberto Mendes da Silva Airport	medium_airport	-25.000323	-53.501208	2481.0	Paraná	Cascavel	POINT (3831718.226 7143997.144)
71	Maestro Marinho Franco Airport	medium_airport	-16.584292	-54.724803	1467.0	Mato Grosso	Rondonópolis	POINT (3695590.675 8140827.535)
83	Ponta Porã Airport	medium_airport	-22.549601	-55.702599	2156.0	Mato Grosso do Sul	Ponta Porã	POINT (3586808.785 7440288.417)
85	Santo Ângelo Airport	medium_airport	-28.282503	-54.169623	1056.0	Rio Grande do Sul	Santo Ângelo	POINT (3757355.633 6737578.539)
90	Brigadeiro Camarão Airport	medium_airport	-12.694400	-60.098301	2018.0	Rondônia	Vilhena	POINT (3097777.568 8585400.848)
106	Santa Maria Airport	medium_airport	-29.711399	-53.688202	287.0	Rio Grande do Sul	Santa Maria	POINT (3810914.745 6556752.132)
149	Corumbá International Airport	medium_airport	-19.011930	-57.672772	463.0	Mato Grosso do Sul	Corumbá	POINT (3367622.840 7858868.554)
155	Rubem Berta Airport	medium_airport	-29.782200	-57.038200	256.0	Rio Grande do Sul	Uruguaiana	POINT (3438220.327 6547725.745)
244	Comandante Gustavo Kraemer Airport	medium_airport	-31.390499	-54.112202	600.0	Rio Grande do Sul	Bagé	POINT (3763743.842 6340901.399)

	ADM0_EN_1	ADM0_PT_1	ADM0_PCODE_1	ADM1_PT_1	ADM1_PCODE_1	ADM2_PT_1	ADM2_PCODE_1	ET_ID_1	ADM0_EN_2	ADM0_PT_2	ADM0_PCODE_2	ADM1_PT_2	ADM1_PCODE_2	ADM2_PT_2	ADM2_PCODE_2	ET_ID_2	geometry
0	Brazil	Brasil	BR	Rondônia	BR11	Cacoal	BR1100049	3	Brazil	Brasil	BR	Rondônia	BR11	Cacoal	BR1100049	3	POLYGON ((2997393.730 8770664.256, 2997393.730...
1	Brazil	Brasil	BR	Rondônia	BR11	Espigão D'Oeste	BR1100098	8	Brazil	Brasil	BR	Rondônia	BR11	Espigão D'Oeste	BR1100098	8	POLYGON ((3056978.535 8777571.933, 3056645.811...
2	Brazil	Brasil	BR	Rondônia	BR11	Presidente Médici	BR1100254	17	Brazil	Brasil	BR	Rondônia	BR11	Presidente Médici	BR1100254	17	POLYGON ((2914558.967 8738429.583, 2915047.868...
3	Brazil	Brasil	BR	Rondônia	BR11	Ministro Andreazza	BR1101203	37	Brazil	Brasil	BR	Rondônia	BR11	Ministro Andreazza	BR1101203	37	POLYGON ((2943634.994 8778259.184, 2949854.901...
4	Brazil	Brasil	BR	Rondônia	BR11	Vilhena	BR1100304	21	Brazil	Brasil	BR	Rondônia	BR11	Vilhena	BR1100304	21	MULTIPOLYGON (((3064936.757 8679749.203, 30646...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
60	Brazil	Brasil	BR	Mato Grosso	BR51	Cláudia	BR5103056	5213	Brazil	Brasil	BR	Mato Grosso	BR51	Cláudia	BR5103056	5213	MULTIPOLYGON (((3633698.340 8745495.507, 36341...
61	Brazil	Brasil	BR	Mato Grosso	BR51	Itaúba	BR5104559	5236	Brazil	Brasil	BR	Mato Grosso	BR51	Itaúba	BR5104559	5236	POLYGON ((3597587.572 8781564.662, 3598001.779...
62	Brazil	Brasil	BR	Mato Grosso	BR51	Nova Santa Helena	BR5106190	5258	Brazil	Brasil	BR	Mato Grosso	BR51	Nova Santa Helena	BR5106190	5258	POLYGON ((3708398.334 8822508.817, 3707916.224...
63	Brazil	Brasil	BR	Mato Grosso	BR51	Feliz Natal	BR5103700	5226	Brazil	Brasil	BR	Mato Grosso	BR51	Feliz Natal	BR5103700	5226	MULTIPOLYGON (((3771432.608 8649297.202, 37709...
64	Brazil	Brasil	BR	Mato Grosso	BR51	Marcelândia	BR5105580	5249	Brazil	Brasil	BR	Mato Grosso	BR51	Marcelândia	BR5105580	5249	POLYGON ((3876654.961 8801397.454, 3876573.477...

Basic spatial operations in Geo Dataframes¶

Formating Geoseries projections¶

Exercise 1¶

Lacking CRS information¶

Exercise 2¶

Projecting Lat/Lon Points¶

Exercise 3¶

Combing GeoDF rows¶

Dissolve¶

Unary Union¶

Exercise 4¶

Creating the convex hull¶

Exercise 5¶

Spatial Overlay¶

Intersection¶

Union¶

Difference¶

Symmetric Difference¶

Exercise 6¶

Validity of Geometry¶